Also referred to as connectionist architectures, parallel distributed processing, and neuromorphic systems, an artificial neural network (ANN) is an information-processing paradigm inspired by the way the densely interconnected, parallel structure of the mammalian brain processes information. Artificial neural networks are collections of mathematical models that emulate some of the observed properties of biological nervous systems and draw on the analogies of adaptive biological learning. The key element of the ANN paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements that are analogous to neurons and are tied together with weighted connections that are analogous to synapses.

Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well. Learning typically occurs by example through training, or exposure to a truthed set of input/output data where the training algorithm iteratively adjusts the connection weights (synapses). These connection weights store the knowledge necessary to solve specific problems.

Although ANNs have been around since the late 1950's, it wasn't until the mid-1980's that algorithms became sophisticated enough for general applications. Today ANNs are being applied to an increasing number of real- world problems of considerable complexity. They are good pattern recognition engines and robust classifiers, with the ability to generalize in making decisions about imprecise input data. They offer ideal solutions to a variety of classification problems such as speech, character and signal recognition, as well as functional prediction and system modeling where the physical processes are not understood or are highly complex. ANNs may also be applied to control problems, where the input variables are measurements used to drive an output actuator, and the network learns the control function. The advantage of ANNs lies in their resilience against distortions in the input data and their capability of learning. They are often good at solving problems that are too complex for conventional technologies (e.g., problems that do not have an algorithmic solution or for which an algorithmic solution is too complex to be found) and are often well suited to problems that people are good at solving, but for which traditional methods are not.

There are multitudes of different types of ANNs. Some of the more popular include the multilayer perceptron which is generally trained with the backpropagation of error algorithm, learning vector quantization, radial basis function, Hopfield, and Kohonen, to name a few. Some ANNs are classified as feedforward while others are recurrent (i.e., implement feedback) depending on how data is processed through the network. Another way of classifying ANN types is by their method of learning (or training), as some ANNs employ supervised training while others are referred to as unsupervised or self-organizing. Supervised training is analogous to a student guided by an instructor. Unsupervised algorithms essentially perform clustering of the data into similar groups based on the measured attributes or features serving as inputs to the algorithms. This is analogous to a student who derives the lesson totally on his or her own. ANNs can be implemented in software or in specialized hardware.

Additional Introductory Material on Neural Networks and Connectionism

Links:

represents a working link, represents a broken link, and represents a verified dead link, as determined during the last link check on 1 June 1997. represents a link added in the last 90 days (i.e., since 24 November 1997) Please help us keep this service useful by letting us know when you change your URLs.

Sources:

• What are Artificial Neural Networks? (Accurate Automation Corporation - AAC)
• What are Neural Networks? (AI Intelligence)
• Artificial Neural Networks (AiMaze)
• A neural model of fear may lead to a better understanding of other emotions (American Psychological Association - APA)
• Network aids psychologists with their business needs (American Psychological Association - APA)
• An introduction to neural networks by Ben Kröse (University of Amsterdam - UvA)
• What Is A Neural Network ? (Attrasoft)
• Réseaux de neurones artificiels (AVENTI - Agence pour la Valorisation et l’Enseignement du Neuro-Traitement de l’Information)
• What are neural networks? (BioComp Systems Inc.)
• Úvod do umelých neuronových sítí (Technical University of Brno)
• Neural Nets by Tom D. Grove (Technical University of Brno)
• Software engineering glossary: Neural Networks (CERN)
• Making BrainWaves (CIO Magazine )
• An Introduction to Neural Networks (David Clark)
• Adaptive Logic Networks Technical Overview (Dendronic Decisions Limited)
• Artificial Neural Systems (Wesley R. Elsberry)
• Genetic Algorithms & Artificial Neural Networks (Wesley R. Elsberry)
• On-line definition of neural networks (Encyclopaedia Britannica)
• Neural networks in financial economics: A breif tutorial (Athanasios Episcopos)
• Introduction to the Self-Organizing Map by Teuvo Kohonen (Helsinki University of Technology)
• A Basic Introduction To Neural Networks (University of Illinois at Urbana-Champaign - UIUC)
• Neural Networks by Christos Stergiou and Dimitrios Siganos (Imperial College)
• Why Neural Networks by Dimitrios Siganos (Imperial College)
• Neural Network Questions and Answers by Dimitrios Siganos and Christos Stergiou (Imperial College)
• Neural Networks, the Human Brain and Learning (Imperial College)
• What is a Neural Network by Chris Stergiou (Imperial College)
• Neural Networks at Your Fingertips (Karsten Kutza )
• Neural Networks (LBS Capital Management Inc.)
• How long before superintelligence? (London School of Economics)
• What is Soft Computing? (Michigan State University - MSU)
• What is an Artificial Neural Network? (NeuroDimension Inc.)
• Glossary of Neural Network Terms (PC AI Magazine)
• Definition of Neural Network (PC Webopaedia)
• Neural net overview by Jochen Fröhlich (Fachhochschule Regensburg )
• Neural Nets by Kevin Gurney (Sheffield University)
• Artificial neural networks: a developing science (Society of Photo-Optical Instrumentation Engineers - SPIE)
• An Introduction to Neural Networks by Leslie Smith (Stirling University)
• Backpropagator's Review (Donald R. Tveter)
• The Basis of AI (Donald R. Tveter)
• The brain as a Darwin Machine (University of Washington)
• Neural Network Definition from the Grolier Electronic Encyclopedia (University of Washington)
• Artificial Neural Networks (West Virginia University - WVU)
• Frequently Asked Questions (West Virginia University - WVU)
• An Introduction to Neural Networks (Z Solutions)

URL: http://www.emsl.pnl.gov:2080/proj/neuron/neural/what.html